Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics
This work addresses the need for better labor market intelligence to help governments and markets adapt skill supply to demand, but it appears incremental as it applies existing methods to new data without major innovations.
The paper tackled the problem of forecasting skill demand using time series data from online job advertisements, employing recurrent neural networks to make multi-step predictions and comparing multivariate versus univariate models, with results showing predictions for IT industry skills but no concrete performance numbers provided.
Rapid technological innovation threatens to leave much of the global workforce behind. Today's economy juxtaposes white-hot demand for skilled labor against stagnant employment prospects for workers unprepared to participate in a digital economy. It is a moment of peril and opportunity for every country, with outcomes measured in long-term capital allocation and the life satisfaction of billions of workers. To meet the moment, governments and markets must find ways to quicken the rate at which the supply of skills reacts to changes in demand. More fully and quickly understanding labor market intelligence is one route. In this work, we explore the utility of time series forecasts to enhance the value of skill demand data gathered from online job advertisements. This paper presents a pipeline which makes one-shot multi-step forecasts into the future using a decade of monthly skill demand observations based on a set of recurrent neural network methods. We compare the performance of a multivariate model versus a univariate one, analyze how correlation between skills can influence multivariate model results, and present predictions of demand for a selection of skills practiced by workers in the information technology industry.